Does cannabis use moderate smoking cessation outcomes in treatment‐seeking tobacco smokers? Analysis from a large multi‐center trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Tobacco and cannabis are frequently used in combination and cannabis co-use may lead to poor tobacco cessation outcomes. Therefore, it is important to explore if cannabis co-use is associated with a reduced likelihood of achieving successful tobacco abstinence among treatment-seeking tobacco smokers. The present study examined whether current cannabis use moderated tobacco cessation outcomes after 12 weeks of pharmacological treatment (varenicline vs. nicotine patch vs. placebo) with adjunctive behavioral counseling. METHODS: Treatment-seeking tobacco smokers (N = 1,246) were enrolled in an intent-to-treat study, of which 220 were current cannabis users. Individuals were randomly assigned to 12 weeks of placebo (placebo pill plus placebo patch), nicotine patch (active patch plus placebo pill), or varenicline (active pill plus placebo patch), plus behavioral counseling. The primary endpoint was biochemically verified 7-day point prevalence abstinence at the end of treatment. RESULTS: Controlling for rate of nicotine metabolism, treatment arm, age, sex, alcohol, and level of nicotine dependence, cannabis users were as successful at achieving biochemically verified 7-day point prevalence abstinence compared to tobacco-only smokers. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Findings suggest that cannabis use does not hinder the ability to quit tobacco smoking. Future tobacco cessation studies should employ prospective, longitudinal designs investigating cannabis co-use over time and at different severity levels. (Am J Addict 2016;25:291-296).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".